Integrated diagnostics and control systems

ABSTRACT

An integrated control and diagnostics system for a controlled system (e.g., a motor) includes a diagnostics module and a controller coupled to the motor. To optimize operation, the diagnostics information signal is used to modify the control provided by the controller as required. Moreover, the output of the control module is coupled to the diagnostics module so that the health assessment made by the diagnostics module can be based at least in part on the output of the controller. The invention uses a model-based diagnostics approach that allows integration of control algorithms with diagnostics algorithms to intelligently trade off optimizing performance to avert or accommodate failures, and to meet performance requirements in a wide range of applications.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed generally to systems that perform bothcontrol and diagnostics operations on a monitored system, and morespecifically, a system in which the diagnostics system is integratedwith the control system to provide optimum control and health assessmentof the monitored system based on the output of both systems.

2. Description of the Related Art

Known control systems typically are feed-forward or feedback systemswhich implement closed-loop control to obtain and maintain certainoperating conditions. Conventionally, control systems are often used tomaintain a prescribed controlled system operating state or conditionsuch as temperature, speed, position, trajectory, torque, etc., or toachieve a prescribed system state such as for motion control androbotics applications. These systems typically implement a stablecontrol law that works to maintain operating performance notwithstandingcertain operational and/or physical constraints and externaldisturbances. Moreover, many of these systems exhibit non-linearcharacteristics. For example, servo actuator systems have inherentnon-linear characteristics. Control design typically requires that anonlinear system be treated as linear, usually by canceling thenonlinearity using feedback. These “linearizing” techniques have notfound wide acceptance because they are generally not robust enough tooperate in real-world applications. Often they are infeasible due to thelarge number of control inputs required to regulate the system.Consequently, these techniques have had relatively little use in solvingreal-world problems. For instance, the non-linearities associated withthe aforementioned servo actuator systems cannot be ignored in controldesign. As such, a control system is needed that accounts for systemnonlinearities using an alternative to correcting for suchnonlinearities with “linearizing” techniques.

Some known control systems utilize non-linear control methods such asmodel-reference (MRAC), gain scheduling, controller scheduling, fuzzylogic, or feedback linearization, to dynamically modify the operation ofthe controller in response to sensed changes in system behavior. Suchchanges in behavior include different system dynamics includingcompliance, noise or other related changes. Moreover, such systems maybe time varying and may be difficult or impossible to model for controlpurposes. Notably, these control systems typically operate in isolationfrom any diagnostic or prognostic systems.

In addition to providing control, some systems implement independentdiagnostics apparatus to monitor the overall health of either theapparatus being controlled, or the control system itself. Some systemsmay have no control, but only machinery diagnostics capabilities.Notably, assessing system health can be used to minimize unscheduledsystem downtime and to prevent equipment failure. This capability canavoid a potentially dangerous situation caused by the unexpected outageor catastrophic failure of machinery. Moreover, some diagnostic systemsinconveniently require an operator to manually collect data frommachinery using portable, hand-held data acquisition probes.

Other known systems have sensors and data acquisition and networkequipment permanently attached to critical machinery for remotediagnostics. Typically the diagnostics equipment is directed todetecting problems with the control system hardware itself or monitoringthe integrity of the output, i.e., monitoring when the control systemresponse is outside prescribed time or value limits. As noted above,control system health monitoring, health assessment and prognosticsgenerally are performed in isolation from any associated control system.These systems typically conduct passive monitoring and assess systemhealth using diagnostic algorithms and sensors dedicated to establishsystem health. This passive monitoring is frequently done usingoff-line, batch-mode data acquisition and analysis to establish thehealth of the system.

For example, in FIG. 1, a conventional prior art automated control anddiagnostics monitoring system 10 for use with a machine 12 that operatesa plant (or as part of a process) is shown. System 10 includes a controlmodule 14 that provides closed loop feedback control of machine 12 tomaintain a setpoint condition (e.g., a velocity). In addition, system 10includes a diagnostics block 16 electrically coupled to machine 12 formonitoring the health of the machine. In particular, diagnostics block16 receives sampled systems data and processes the data to assess thehealth of the machine 12. A primary drawback of such a system is thatdiagnostics block 16 operates independent and isolated from controlmodule 14 and performs off-line diagnostic processing which is notreadily adaptable to integration with on-line control.

However, as noted previously, because virtually all diagnostics systemsperform off-line diagnostic processing, it has been extremely difficultto implement diagnostics processing real-time in coordination withon-line control. Presently, no system exists which integrates controland diagnostics to optimize control outputs dynamically in real-time.

As a result, the art of control and diagnostics systems is in need of acontrol and diagnostics system that advantageously utilizes the outputsof each system to optimize the performance of both systems. Such asystem would be able to dynamically optimize the operation of thecontrolled system by accurately diagnosing problems and predicting thefuture state of the controlled system based on health data fromdiagnostic sensors and/or from the control system. This would enable thesystem to alter the control operation in a goal-directed manner tofacilitate diagnostics and prognostics, to reduce or eliminate excessivewear or degradation of the controlled system, or to achieve otheroperational objectives.

SUMMARY OF THE INVENTION

Notably, it has been determined that the information developed throughthe use of a diagnostics system is particularly valuable in assessingwhat type of control action should be applied to the monitored system.Vice versa, the output of the control system, which is based oncontrolled system response, is valuable in determining the overallhealth of the controlled system.

The preferred embodiment overcomes the limitations associated with priorsystems which perform control and diagnostics operations independentlyon a controlled unit by utilizing advances in machinerydiagnostics/prognostics in conjunction with conventional controlhardware and non-linear/adaptive control techniques to provide acompact, cost-effective and intelligent system. The system of thepreferred embodiment provides a tight coupling of embedded hybriddiagnostics and control to achieve optimum system performance inconjunction with reliable prognostics to facilitate maximizing machinerylongevity and lowest cost of ownership. The integrated diagnostics andcontrol elements of the present system allow efficient operation of thecontrolled system over its lifetime by intelligently predicting thetime-life trajectory of the controlled system and altering operationaccordingly.

The present invention readily utilizes existing architectures such asintegrated motor-drive and motor-drive actuator systems which providefurther enhanced operation by extending the life of these controllerswith the use of model-based and qualitative/causal model information toprovide intelligent control and diagnostics. More particularly, themodel-based diagnostics approach of the present invention allowsintegration of the control algorithms with diagnostics algorithms tointelligently trade off optimizing performance to avert or accommodatefailures, and to meet demanding performance requirements in a wide rangeof application environments. Overall, the result of implementing thesefeatures is a coherent, coupled control and diagnostics system thatoutperforms known systems having independent diagnostics and controlapparatus operating in isolation.

According to a preferred embodiment, an integrated control anddiagnostics system for a controlled unit includes a diagnostics moduleintegrated with the motor that generates a diagnostics informationsignal indicative of the health of the motor. In addition, the systemincludes a controller integrated into the motor. To optimize operation,the diagnostics information signal is used to modify the controlprovided by the controller, as required. Moreover, the output of thecontrol module is coupled to the diagnostics module so that the healthassessment made by the diagnostics module can be based at least in parton the output of the controller and the systems response to this controlaction.

According to another aspect of the invention, the controller isassociated with at least one changeable parameter, the parameter beingchangeable in response to the diagnostics information signal. Moreover,the controller preferably includes a velocity feedback loop and a torquefeedback loop to implement the control.

According to yet a further aspect of the preferred embodiment, theintegrated control and diagnostics system includes an enhancement modulethat generates an evolving set of design rules based on a plurality ofthe diagnostics information signals so as to facilitate designing animproved version of the motor and drive system. The enhancement modulepreferably includes a memory having a model embedded therein to generatethe evolving set of design rules according to user specifications.

According to another aspect of the invention, the method of optimizingcontrol and diagnostics operations performed on a controlled unitincludes the steps of providing a diagnostics module and a controlmodule, each of which is integrated with the controlled unit. Inaddition, the method includes the step of generating a control signalwith a control module in response to feedback from the controlled unitand generating a diagnostics signal indicative of the health of thecontrolled unit, with the diagnostics module, based on the controlsignal. Also, the method includes the step of predicting when acontrolled unit failure will occur, as well as the cause of thecontrolled unit failure. Finally, the method includes the step ofdetermining whether to alter the control signal based on the predictingsteps.

According to yet a further aspect of the preferred embodiment, a methodof optimizing the useful life of a motor according to a preventivemaintenance schedule that includes a plurality of preventive maintenancecheckpoints includes the step of sensing a motor parameter duringoperation of the motor. Thereafter, the method includes generating acontrol signal with a controller based on the motor parameter and thendiagnosing a health condition of the motor based on at least one of anoperating objective, the control signal and a process constraint. Themethod also predicts, based on the health condition, whether a motorfault condition will occur prior to the next preventive maintenancecheckpoint and then determines whether to alter the control signal inresponse to the prediction. If the altered control signal is thenissued, the method will again determine if a motor fault will occurunder the new control scheme prior to the preventive maintenancecheckpoint, and then prescribe any necessary change in the control.

These and other objects, features, and advantages of the presentinvention will become apparent to those skilled in the art from thefollowing detailed description and drawings. It should be understood,however, that the detailed description and specific examples, whileindicating preferred embodiments of the invention, are given by way ofillustration and not of limitation. Many changes and modifications maybe made within the scope of the present invention without departing fromthe spirit thereof, and the invention includes all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS

A preferred exemplary embodiment of the invention is illustrated in theaccompanying drawings in which like reference numerals represent likeparts throughout, and in which:

FIG. 1 is a block diagram of a prior art system including independentdiagnostics and control apparatus;

FIG. 2 is a generic block diagram of an integrated diagnostics andcontrol system according to a preferred embodiment of the presentinvention;

FIG. 3 is a more detailed block diagram of the system shown in FIG. 2;

FIG. 4 is a circuit diagram of a system according to a preferredembodiment of the present invention;

FIGS. 5A and 5B is a flow chart showing the operation of the systemshown in FIG. 4;

FIG. 6 is a generic block diagram showing an alternate embodiment of thepresent invention; and

FIG. 7 is a block diagram showing n alternate configuration of apreferred embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Turning initially to FIG. 2, a controlled system 20 (e.g., a motor)includes an integrated control and diagnostics system 22 for operating aplant or process 21. System 22 includes both a control module 24 and adiagnostics module 26 in a tightly coupled hybrid configuration. Withthe integrated configuration, the control and diagnostics modules 24 and26, respectively, are preferably packaged in a single unit and thus ableto share a common set of sensors 28 to collect system data, and a commonbus or shared memory to exchange data and information. Sensors 28preferably comprise a plurality of sensors embedded in controlled system20 to measure parameters associated with controlled systemelectrical/mechanical components 27 (for example, stator windings)including, inter alia, motor speed, motor current, voltage, temperature,vibration, magnetic flux, and lubrication characteristics. Althoughpreferably implemented with cost-effective, off-the-shelf sensors,sensors 28 can be implemented with sensor technology that includesintelligent sensors, self-validating sensors and smart sensors withembedded diagnostics. In one preferred application, the hardwarecomponents of the system shown in FIG. 2 can be assembled to comprise anintelligent servo motor including a brushless DC motor with integratedcontrol, diagnostics, communications and power electronics.

Control module 24 is communicably integrated within controlled system 20in a closed-loop feedback configuration and outputs an appropriatedriving output to maintain operation of controlled system 20 at apredetermined setpoint value (e.g., at a particular velocity). Inoperation, control module 24 receives a data signal from sensor 28 andprocesses that signal to generate a driving output. In addition toapplying the driving output to controlled system 20, system 22 can usethe driving output to facilitate generation of an assessment of thehealth of the controlled system 20, or perform a system simulation. Thehealth assessment is generated by an algorithm designed according to aparticular model associated with controlled system 20, as discussed infurther detail below. Integrated diagnostics module 26 is preferably ofthe type shown and described in U.S. Pat. No. 5,917,428 to Discenzo etal., which is expressly incorporated herein by reference. The moduledisclosed in Discenzo et al. uses an onboard processor and sensors toprovide diagnostics for motor bearings and other mechanical andelectrical components based on current, voltage, stator temperature,vibration measurements, etc. In addition to indicating the health ofcontrolled system 20 to the operator, an output of diagnostics module 26may be applied to control module 24, preferably as a recommended changein control when necessary.

Overall, integrated control and diagnostics system 22 operates toefficiently utilize both control and diagnostics features to maximize,for example, the time-life trajectory (i.e., a relationship showing howthe performance of the controlled system changes over time and eventualsystem failure and under various operating parameters) of controlledsystem 20 according to detailed user specifications. For example, if theoutput of diagnostics module 26 indicates that motor stator windinginsulation failure is imminent, the drive algorithm (embedded in controlmodule 24, described below) can be dynamically reconfigured accordinglyto reduce the possibility of a premature, inconvenient or hazardousoutage. Conversely, diagnostics can be optimized by stimulating thesystem via actuation of the control and thereafter observing the systemresponse.

Next, as shown in FIG. 3, a more detailed schematic of a system 30includes an integrated diagnostics and control system 29 having acontrol module 24 and a diagnostics module 31 coupled to a controlledsystem 20. The hardware of control and diagnostics system 29 ispreferably modular and is based on application specific integratedcircuits (ASICs), advanced power control, and implements thermalmanagement techniques. Further, by using a Silicon-on-Insulator (SOI)process to achieve higher voltage isolation, the control logic, gatedriver, and power switches remain integrated on a single chip for lowpower applications up to 155V. On the other hand, the control and powercircuits are separated into distinct components for higher voltage/powerapplications. The power converter (not shown) uses 460V 3-phase ACsupply and is preferably designed for integration with a twenty-fivehorsepower induction motor. In the preferred embodiment, the powerconverter employs power switching control to reduce the need for largeDC-link capacitors and utilizes micro-channel cooling structures toreduce the size of the heat sink. The control algorithm is based on thesystematic use of small controls or low-gain design techniques thatexploit the inherently dominant nonlinearities of the dynamic system,rather than using feedback controls incorporating approximation orfeedback linearization. The main purpose of the feedback control in thepresent system is to regulate the system in the large and in a mannerthat is not sensitive to various perturbations, disturbances anduncertainties in the system.

Controlled system 20 is monitored by diagnostics module 31 that ispreferably implemented with an ASIC that provides a flexible sensorinterface and diagnostic signal processing functions. In operation,diagnostics module 31 continuously obtains data (e.g., relating totemperature, velocity, etc.) from controlled system 20 via sensor(s) 28coupled thereto. More particularly, diagnostics module 31 performs ahealth assessment of controlled system 20 based on the sensor data andgenerates a corresponding signal indicative of the health of controlledsystem 20. In general, the health assessment signal is indicative of anydeviation in controlled system operation from one or more normaloperating characteristics associated with the controlled system.Notably, sensors 28 may be supplemented with specialized algorithms thatutilize controlled system 20 as a probe to diagnose problems associatedwith the connected plant or process, or sensors 28 may employ virtual orcomputed sensor functions (for example, to reduce sensor needs), such asthat shown and described in U.S. Pat. No. 5,995,910 to Discenzo entitledMethod and System for Synthesizing Vibration Data, which is expresslyincorporated herein by reference.

With further reference to FIG. 3, diagnostics module 31 is coupled to adecision making module or processor 32 (which may be integrated withdiagnostics module 31), for transmitting the health assessment signal tothe control module 24 or to an external indicator unit 34 or to theoperator. Processor 32 processes the health assessment signal,preferably according to a particular model or suite of models, todetermine necessary system changes (e.g., in terms of the controlprovided by control module 24) to optimize operation. In addition,optimization parameters including job specific, and/or mission or systemspecific parameters may also be input to processor 32 via auxiliaryinputs 35. For example, the optimization parameters may designaterequirements relating to mission requirements, workload schedule,environment, preventive maintenance schedules, etc. Together with thehealth assessment signal, the optimization objective(s) and operatingparameters and constraints can be used to facilitate generation of asystem recommendation signal to alter the control supplied by controlmodule 24.

More particularly, control optimization, as well as health assessment,are processed according to an algorithm(s) executed by processor 32. Thealgorithm is preferably designed according to the specifications ofcontrolled system 20 and accounts for parameters such as susceptibilityof the controlled unit to degradation over time and under certainoperating conditions. In general, model-based techniques are used thatincorporate critical information about the dynamic characteristics ofcontrolled system 20. The model optimizes the ability of the system tosimultaneously detect and diagnose incipient faults before they renderthe system inoperable, and then alter the control to accommodate theanticipated faults accordingly. The preferred approach integratesconventional techniques such as Discrete Fourier Transform (DFT), GaborTransform, Wigner-Ville distribution, etc. with more advanced techniquessuch as wavelet decomposition, non-linear filtering-based statisticaltechniques, and analysis of time series data using non-linear signalprocessing tools such as Poincare' maps and Lyapunov spectrum.

Poincare' maps and Lyapunov spectrum are analytic techniques thatprovide a unique view or window into non-linear dynamical systems. Thesetechniques indicate the degree of chaotic behavior in what appears to bea random process and can characterize the chaotic mapping derived as anindicator of system operating state. For example, some mechanicalsystems have been shown to exhibit an increase in chaotic behavior withthe onset of mechanical instability due to lubrication problems.Poincare' maps and Lyapunov numbers, among others, are derivedanalytically from sampled time series data of the type available todiagnostics module 31.

For example, both windowed Fourier transform and the wavelet transformhave time-localization properties that are important in the diagnosis ofrotating machines. It is known that the “graceful” degradation of abearing in a rotating machine can lead to packets of high frequency datathat are localized in time. This can be interpreted as a signature ofthe failure mechanism, and identification is dependent on extractingthis signature from the real-time sensor data. Notably, wavelettransform is well suited for the time-frequency decomposition of signalsthat have short-lived high frequency components, and that aresuperimposed on a lower frequency carrier. This type of data isgenerated by system failures such as wear induced contact in thebearings and tooth damage in the meshing gears. Furthermore, recentdevelopments in wavelet based de-noising schemes are utilized to extractmeaningful information from data obtained from noisy sources, such asthe interior of the rotating machinery. The wavelet transformestablishes a series of basis functions (e.g., low order polynomials)and these functions are used to represent the time series data. Thecareful choice of basis functions provides information regarding boththe time domain and frequency domain characteristics of the data. Aperiodic impulse would be smeared across all frequencies in a FourierTransform and be indistinguishable. Alternatively, using time domaintechniques requires frequent data sampling and data evaluation in orderto not miss detecting the occasional impulse event. Wavelet transformtechniques provide for joint time-frequency analysis by analyzing theresultant basis functions and their coefficients. Therefore, for sometypes of machinery failures, incipient fault detection using wavelettransform is preferred over the windowed Fourier transform.

Moreover, when controlled system 20 comprises rotating machinery, chaostracking techniques are also used in the diagnosis approach of thepreferred embodiment. An important ability of chaos tracking techniquesis to adaptively correlate patterns in the vibration orbit trajectorieswith certain problems in rotating machinery. Using such a chaos trackingtechnique, the preferred embodiment models the physical mechanisms thatcan lead to machine problems. More particularly, for each particularproblem to be diagnosed (for example, rub-impact in a bearing orunbalance in a rotor), a mathematical model is developed. The modelsused by the present invention have adjustable parameters developed withcomputer simulation that account for a wide variety of applications.Chaos tracking templates are then used for each detection and diagnosisscenario.

In operation, detection and diagnosis can be accomplished with patternmatching. That is, given vibration data from the controlled system 20,chaos tracking methods (e.g., generating a Poincare' map) process thedata and generate a pattern for the current operating condition. Themethod of the present invention then compares this pattern with thevarious templates that have been developed through experimental testingand simulation. Statistical correlation and pattern matching techniquesare then used to determine the most likely operating scenario, and thusto indicate the health of the system. For example, based on historicaldata along with device and process physics, we may establish a set ofprobabilities such that given a series of observable symptoms, we willascribe a certain (e.g., fault mode) health state to the system with aparticular probability. Based on this and additional information, system30 improves this health assessment. For example, techniques such asDempter-Shaefer Theory can be used to establish this more accuratehealth assessment. Other techniques such as artificial neural netclassifiers, as are well known in the field, can also be included inthis preferred implementation.

For example, some notable problem sources due to vibration in a motorinclude loose rotor-bearing support or components, rub/impact phenomenabetween the rotor and the non-rotating surface, propagating cracks in adynamically participating flexural component (e.g., a shaft). Otherproblem sources include backlash, play (dead band), Hertzian contacts orbilinear flexibility, and non-linear resistive, inductive or capacitiveelements including motors, generators and electro mechanical networkinteractions.

Notably, the embedded models may also implement “rules of thumb”associated with the system. For example, such a rule may indicate thatfor a particular controlled system 20 the system may lose half its lifefor every ten degree increase in temperature. Overall, the presentinvention accounts for all the above-described rules, objectives andconstraints so as to intelligently generate a system recommendation foroptimum controlled unit operation.

Next, the recommendation may include generating a signal, for example,to activate an emergency shut down, to indicate when to replace thebearings, etc. Most typically, diagnostic/prognostic information will beused to alter the controller or control strategy to avert or accommodatefaults. For example, the control can be modified to allow a weakenedcomponent to survive under a smoother, but less efficient control law.As a result, the mission can be completed under less equipment stressconditions until the next scheduled shut down of the controlled system20 (e.g., preventive maintenance checkpoint) or other convenientshutdown time. Notably, the recommendation signal generated from thedecision-making module can be used to direct a reconfiguration of thesystem and its operation. Such a reconfiguration will enable meeting thesystem operating goals and lifetime requirements in a manner superior tothat achieved by merely changing the control. For example, with multiplemotor-pumps controlling a ship's change in ballast, system 30 canreconfigure the piping to load share with the motor which can be proneto excessive heating or cavitation when running at full pump speeds.This allows dynamically extending machinery lifetime and meetingperformance requirements in a manner superior to just changing machinerycontrol.

Generally, processor 32 operates to dynamically alter parametersassociated with control module 24 coupled thereto, thus providingmulti-objective optimization of the control. By intelligently alteringcontrol in this fashion, a variety of control functions may be realizedincluding enabling stimulus-response analysis, altering the time-lifetrajectory associated with controlled system 20, predicting safeoperation beyond normal operating regimes, and executing more efficientand accurate control output, etc. For example, stimulus-responseanalysis can be activated based on the recommendation (or a uniquecontrol action via, for example, and impulse or step) to prove ordisprove a particular diagnostic hypothesis by (1) causing the controlsystem to excite a particular condition within the system, and then (2)analyzing the system response. Knowing the control action and resultingsystem response enables system 30 to generate an enhanced model of thesystem for diagnostics; for example, system 30 can be implemented toperform system identification and establish a plant transfer function.Also, because the control provided by control module 24 is dependent onthe diagnostics/prognostics, system 30 provides redundancy in the eventthat some control components (e.g., sensors) fail.

In addition to the recommendation generated by processor 32, processor32 generates an indicator signal to notify the operator of a particularcondition via indicator unit 34 (e.g., an indicator alarm) coupledthereto. Notably, processor 32 may also send the signal to anothercomputer system such as a maintenance management system (CMMS), or toanother diagnostics or decision making module for coordinating optimumoperation of multiple systems.

The control provided by control module 24 is also dependent upon anyoperating objectives and constraints either input by the user orgenerated by system 30 itself. For example, if the data provided bysensors 28, and processed by diagnostics module 31, establishes that aparticular frequency excites a resonance within the system thatcompromises the integrity of the output, control module 24 accommodatesthis process constraint by providing an appropriate control signal toavoid system operation at that particular frequency. For example, thedrive characteristics can be changed in a motor-driven system to shiftlosses from a weakened motor to a stronger inverter by shifting to alower PWM frequency at the expense of less efficient system operation.

Alternatively, the system may require asymmetric firing of a three-phasemotor (i.e., the controlled system) to minimize the stress exerted on aparticular phase of the three-phase windings of the motor. Yet anotherexample of an operating objective is restricting the rate of currentincrease to a certain amount to optimize system performance, forexample, in terms of longevity. Other constraints/objectives includemaximum throughput, lowest wear rate, lowest costs of ownership(including energy utilization), etc. How these operations areimplemented is discussed in further detail below.

Also shown in FIG. 3 is an enhancement system 36 coupled to integrateddiagnostics and control system 29 and including adiagnostics/prognostics module 38 and a design module 39. Enhancementsystem 36 provides an evolving database of design rules and objectivesto facilitate determining beneficial modifications to the design ofcontrolled system 20 to enhance the ability of future generations ofcontrolled system 20 to perform early failure detection and compensatingcontrol. During operation, health assessment signals generated bydiagnostics module 31 are transmitted to diagnostics/prognostics module38. Module 38, during an off-line batch mode process, processes thehealth assessment signals to generate a diagnostics/prognostics signalthat is input to design module 39. In response, design module 39develops a continually evolving database of design rules.

For example, to aid in early insulation failure detection (if thisoccurs frequently), the design may include a separate winding withreduced insulation which is not essential to the motor operation. If PWMfrequency is frequently being shifted, hardware and software changes maybe required for this to be done more readily, and over a wider frequencyrange. These rules can then be compiled for use by theengineers/operators in designing future generations of controlled system20. For example, based on the health assessment and the operatingconditions, design module 39 may determine that a component of thecontrolled unit needs to be made according to different specificationsand tolerances in view of predetermined parameters, e.g., costs,reliability, etc., when being operated under the same or similarconditions. Generally, some benefits realized by enhancement system 36include longer control system life, increased fault tolerance within thecontrol system, facilitating prognostic analysis, more effectivelycompensating control, reduced catastrophic failures, lower repair cost,etc.

Turning to FIG. 4, a schematic of an integrated diagnostics and controlsystem 40 is shown. System 40 receives an excitation signal and includestwo feedback loops 42 and 44 to implement control, and a diagnosticsloop 46 for monitoring the health of a controlled unit 48. Controlledunit 48 can be, for example, a motor that drives a plant or process 49such as a pump. In operation, controlled unit 48 is monitored withsensor(s) 28 (e.g., position/velocity sensor) that outputs a velocityfeedback signal indicative of the actual velocity to a comparisoncircuit 50 that compares the velocity feedback signal to a predeterminedsetpoint value (e.g., a desired velocity). Comparison circuit 50generates a first error signal, a velocity error signal, with thevelocity error being equal to the difference between the velocityfeedback signal of the controlled unit 48, and the setpoint. Thevelocity error signal is then applied to a system controller 52 thatgenerates a control signal and transmits the control signal tocontrolled unit 48. System controller 52 receives the velocity errorsignal and generates a command or reference signal that is applied tocontrolled unit 48 to minimize the velocity error.

More particularly, system controller 52 includes a first controller 54(e.g., a P-I controller) that receives a velocity command signal andgenerates a current reference signal that is applied to second feedbackloop 44. Second feedback loop in turn, generates the control signal thatis applied to controlled unit 48 to maintain operation at the setpoint.The second feedback or torque control loop 44 provides a “currentfeedback” signal indicative of the actual motor current obtained fromcurrent sensors preferably mounted within the motor, although they maybe integral to the inverter and remote from the motor. Torque controlloop 44 minimizes the “torque error” by initially determining the torqueerror by comparing the current reference signal generated by firstfeedback loop 42 to the current feedback signal with a second comparisoncircuit 56. In other words, the torque error is the difference betweenthe actual current that flows through the motor and the referencecurrent. The torque error is then applied to a controller 58 (e.g., aP-I controller) which processes the current error to generate a modifiedcurrent signal that compensates for the torque error signal that isapplied to an inverter 60. Inverter 60 then processes the modifiedcurrent signal to generate the driving output (current or voltage) thatis applied to controlled unit 48 to achieve the desired controlled unitoperating state. Notably, because current and torque are directlyrelated, current control loop 44 effectively implements a torque controlloop, similar to velocity control loop 42. In sum, the control signaloutput by controller 58 (and specifically current control loop 44)minimizes the current error and controls the voltage applied to, forexample, the motor windings of controlled unit 48.

The output of sensor(s) 28 is also coupled to a diagnostics module 26which provides a health assessment of controlled unit 48 based on theoutput of sensor(s) 28 (e.g., position/velocity, torque, etc.), and acontrol recommendation is generated based on the health assessment.Notably, the health assessment preferably is based not only on theoutput of sensor(s) 28, but also on the control signal output bycontroller 52 to minimize current error (described above), and thecontrol recommendation output by diagnostics module 26 itself. Further,diagnostics module 26 can consider the previous control commanded to themotor 48, along with the current state of motor 48, to get a betterindication of motor health and thus generate, for example, anappropriate transfer function.

In operation, motor 48 is continually monitored for health as sensor(s)28 provides updated data so as to allow diagnostics module 26 tocontinually modify and optimize system control. Further, system 40 canbe used to maximize the integrity of the health assessment based on thecontrol provided, as described above. Note that the system componentsshown in FIG. 4 can be implemented independent of motor 48 or mountedintegral with the motor or an actuator.

With reference to FIGS. 5A and 5B, a program 70 for operating integratedcontrol and diagnostics system 40 shown in FIG. 4 is shown. Program 70is an adaptive model that utilizes feedback to actively control, interalia, the time-life trajectory of the system. After initialization andstart up at Step 72, program 70 determines whether the controlled system(e.g., a motor) is activated at Step 74. In the event that the program70 determines that controlled system is not activated in Step 74, theintegrated controller/diagnostics system of the preferred embodiment maystill execute diagnostics operations based on data collected by thesystem sensors. For instance, the environment in which the system isimplemented may need to be considered to accurately assess motor healthand, in particular, vibration in the environment may pose a significantthreat to efficient motor operation. Therefore the effects of suchvibration should be monitored, even when the controlled system is notoperating.

If the controlled system is activated, motor parameters such astemperature, current, etc. are sampled at Step 76 and transmitted to adiagnostics module (26 in FIG. 4) to determine the health of thecontrolled system at Step 78. Thereafter, at Step 80, program 70determines the remaining lifetime of the controlled system and likelyfailure cause based upon the health assessment performed in Step 78.After the determinations in Step 78 and 80 are complete, a decision ismade as to whether the motor will live to the next preventivemaintenance check (PM) at Step 82. Not only is the determination made inStep 82 dependent upon the remaining motor lifetime and projectedfailure cause determined in Step 80 but, in addition, is based onvariables such as the rated duty associated with the controlled system,the time to the next preventive maintenance check, the environment inwhich the controlled system is operating and is expected to operate inthe future, etc., which may be input by the operator or comprise part ofthe model and mission associated with the controlled system.

In the event that system 70 predicts that the controlled system will notreliably operate to the next preventive maintenance checkpoint orcomplete the needed mission or job run, program 70 executes Step 84 to(1) determine what changes in control will provide the desired systemoperation, and (2) generate a control recommendation. In addition to thedeterminations made in the previous steps, the control recommendationcan be based upon operating variables including process constraints(e.g., avoid operating at a particular frequency) and operatingobjectives. The constraints preferably are input by the user ordetermined by the integrated diagnostics, machinery and process models,and wear and degradation models, as well as the control system itselfbased on the sensor data. The constraints can indicate an amount ofallowable risk and the certainty of normal operation required. Forinstance, knowing that the system will not accept any risk of completeshut down, the program can change the control to insure that the systemwill be one.

In response to the control recommendation, program 70 determines a newexpected time to fail (similar to the determination made in Step 80) atStep 86. If the system will reliably operate until the next preventativemaintenance checkpoint or complete the needed mission, as predicted inStep 88, a controller modification signal is generated and transmittedto the controller (e.g., 52 in FIG. 4) in Step 90 to change thecontroller parameters (e.g., parameters of inverter 60 in FIG. 4)according to the control recommendation (Step 84). Then, in Step 92, thecontroller is activated to receive the controller modification signal.Notably, a separate controller activation step is preferred to accountfor any potential stability problems. In particular, at Step 92 program70 makes a determination as to whether the system will fail or gounstable with the planned change in control so as to accommodate thechange accordingly.

In the event that program 70 determines, at Step 88, that the systemwill not reliably operate according to the process constraints, motorspecifications, etc., until the next preventive maintenance checkpoint,the controlled unit operator will be notified of the predicted failureprior to the next preventive maintenance check at Step 89. In addition,at Step 89, program 70 still preferably outputs a signal to thecontroller (Step 90) to alter the controller according to the controlrecommendation determined at Step 84. As a result, the system maximizescontrolled unit operating lifetime under the existing operatingconstraints, notwithstanding the predicted failure prior to the nextpreventive maintenance check. The expected time to fail determined inStep 86 is also saved and used in Step 80 in future program iterationsto in fact confirm that the equipment will operate until the nextpreventive maintenance checkpoint. If not, changes in future iterationsof Step 84 will be made.

Next, if in Step 82 the program 70 determines that the controlled systemwill reliably operate until the next preventative maintenancecheckpoint, optimization criteria including lifetime, overrating,efficiency objectives are considered at Step 94, thus allowing theoperator to customize operation of the controlled unit. Suchoptimization criteria may include detailed operational requirements suchas operate the motor at 200% duty in one hour periods. Otheroptimization criteria may be indicative of the user's desire to run themotor as long as possible without considering efficiency issues. Or, theoperator may not care if the motor dies in a relatively short period;rather, the user may just want to minimize, for example, energyconsumption. Still other optimization criteria may include operate atlowest cost, longest operating time, higher efficiency maximizethroughput, lowest lifecycle cost, etc.

If no optimization criteria are input to the system, program 70 does notmake any additional determinations regarding change in control as thesystem will last to the next preventive maintenance checkpoint asdesired. Program 70 then returns operation to initialization and startup at Step 72. However, if other optimization criteria/operationalconstraints are to be considered, program 70 executes Step 96 toprescribe a change in the controller to meet these criteria. Notably,the closed-loop analysis of the system after prescribed controllerchange allows enhancing control and prognosis algorithms. The prescribedcontroller change is implemented with linear or non-linear programming.

Conventional optimizing control and dynamic optimization techniques canalso be scheduled for implementation in Step 96. We may shift gains tominimize the maximum current draw or, in fact, implement a fuzzy logiccontroller to smoothly implement new control objectives. The prescribedchange in the controller in this case may consist of shifting the fuzzysets used in the fuzzy logic-based controller, for example.

Thereafter, in Step 98, program 70 determines whether the prescribedcontroller change (determined in Step 96) will ultimately meet theoperating objectives and constraints. If the constraints considered inStep 94 will be met, a signal indicative of the prescribed controllerchange is generated and transmitted to the controller at Step 100.Thereafter, at Step 102, the controller is activated, as describedabove.

Alternatively, if the system will not meet the constraints input at Step94, the operator is notified at Step 104 and, in Step 106, adetermination is made as to whether the controller should be alteredanyway. In the preferred embodiment, the program will alter thecontroller anyway according to the prescribed controller changegenerated in Step 96 to achieve the longest controlled system lifetimewithout compromising system performance. Nevertheless, if program 70determines that the controller should not be altered in Step 106 theexecution of the program is again returned to initialization and startup at Step 72.

An important aspect of the preferred embodiments is that the models usedto predict controlled unit lifetime, etc. are adaptive models, ratherthan open loop or feed-forward models. If, for example, the controlledunit does not run as cool as predicted in response to a correspondingchange in control, the integrated diagnostics and control system willalter the model (for example, embedded in diagnostics module 26 in FIG.4) so that the next time program 70 executes a step calling for aprediction, the prediction will be more accurate. For example, thelifetime prediction model may be altered in an automatic, adaptivemanner based on the previously predicted lifetime of the system and itsobserved rate of degradation or aging. This may consist of alteringcoefficients which reduce the forecasted lifetime of machinery based onthe number of degrees over nominal temperature. Alternatively, anon-linear model in time (e.g., an iterative model) may be developed,and refined empirically based on an observed rate of system degradation.

In sum, program 70 uses an adaptive model to predict and record a newtime to fail and, then the model is tested to determine if theprediction was correct. As discussed above, the model may account forhow insulation degrades with temperature, how bearings degrade withvibration and/or temperature, how lube degrades, etc. In addition,parameters such as the environment and predicted future environment willchange such that the predictions must account for such changes and theuncertainty in environment projections (for example, at Step 98). For aparticular environment, and for a certain model associated with thecontrolled unit, the model predicts how the controlled unit will operateand how the controlled system will respond to different stressesproduced by the environment. Then, upon each iteration of program 70, alife history of the model is generated so as to facilitate thedevelopment of an improved model for the system, and to optimize thetime-life probability curve associated with the controlled unit.

Turning to FIG. 6, an integrated control and diagnostics system 110according to an alternate embodiment provides optimization of thecontrol and diagnostics operations via a supervisory system 112. Similarto the preferred embodiment described above, system 110 includes controland diagnostics modules 24 and 26, respectively, that are integratedwith each other and, for example, mounted within a controlled system111. Preferably, module 24 and 26 comprise application specificintegrated circuits (ASICs), however they can all be implemented insoftware. And, sensors 28 are coupled to mechanical and/or electricalcomponents 27 of controlled system 111 to continuously monitor systemoperating parameters.

Supervisory system 112 provides centralized overall health assessmentand operational optimization. Supervisory system 112 comprises a mainprocessor that is integrated with control and diagnostics modules 24,26, respectively, but it may comprise a separate unit. Supervisorysystem 112 includes a plurality of operator inputs 114 which may includea setpoint (e.g., a velocity), goals/objectives (e.g., running at 200%duty intermittently to minimize energy costs), and other miscellaneousconstraints (e.g., minimum required motor speed, spare parts inventory,costs, etc.). Supervisory system 112 utilizes an adaptive model 116associated with controlled unit 111, in conjunction with individualsub-modules 118 to optimize system operation. Sub-modules 118 mayinclude an evaluation module, an action planning module, a hypothesisgeneration module, an optimization module, a simulation/forecastingmodule, a scheduled change module, etc. Each of sub-modules processes aparticular set of data from both operator inputs 114 and system stateinformation generated by diagnostics module 26 and controller statusinformation from control module 24. Based on the processing performed bysupervisory system 112, system 112 optimizes system operations by (1)modifying the output of control module 24 based on controlrecommendations output by diagnostics operations, and (2) making moreaccurate health assessments based on the control applied to thecontrolled unit, as described above.

The control electronics and diagnostics electronics of system 110 areintegrated on a single logic board. Two ASICs are used for theintegrated control/diagnostic board. The ASIC associated with controlmodule 24 performs low level motor control functions, includinggeneration of pulse-width-modulation (PWM) signals to switch the powerelectronics on the power converter board. The ASIC associated withdiagnostics module 26 monitors multiple sensor inputs and performssignal processing, including scaling, filtering, and computation of FastFourier Transform (FFT) for raw sensor data. Both ASICs also contain“glue logic” for accessing the onboard A/D converter, MUX and memory(not shown). Glue logic is a means for modules 24 and 26 to utilize oneset of sensors and to readily share common data. This may be done usingshared memory, a common bus, and time and event synchronizing signals.Similar techniques will be used to allow asynchronous operation of thesub-modules in 112. The ASICs operate in conjunction with main processor112. The ASICs function primarily to offload repetitive low-levelcomputations from main processor 112, thereby freeing main processor 112to focus on high-level pattern classification, optimization, anddecision-making tasks. Main processor 112 receives post-processed datafrom the ASICs of the control and diagnostics modules 24 and 26,executes condition-based maintenance and integrated control/diagnosticalgorithms, and modifies controller parameters as necessary. The ASICsare preferably implemented in Field Programmable Gate Arrays (FPGAs) toreduce development time and to accommodate design modifications(described above in conjunction with FIG. 3.)

Note that the controlled system of the preferred embodiments has beendescribed for use with a motor; however, other types of controlledsystems 20 contemplated for use with one preferred embodiments includeovens, hydraulic systems, internal combustion engines, completevehicles, etc.

In addition, multiple ones of the systems of the preferred embodimentsmay be integrated to form a hierarchy of control and diagnostics. Forexample, as shown in FIG. 7, a system 130 includes a plurality ofintegrated control and diagnostics systems (e.g., system 110 in FIG. 6)that are electrically coupled to a coordination module 132. Module 132is adapted to provide higher level decision making to efficientlyoperate at least one plant or process 21. Coordinating the control andhealth assessment is preferably implemented with a system model executedby module 132. Module 132 functions to maximize the useful life of eachcontrolled system by assessing the health and control provided for eachsystem. As a result, system 130 operates to insure that changes incontrol associated with one controlled system do not adversely affectother controlled systems in the overall process line.

Overall, the integrated diagnostics and fault-tolerant control featuresof the preferred embodiment make an intelligent servo motor particularlysuited for industrial manufacturing operations where reducing systeminstallation time and avoiding unscheduled down time are critical toprofitability. Similarly, the system of the preferred embodiments alsoimproves combat readiness of electric actuators on military platforms.In particular, based on expected mission requirements, thediagnostics/prognostics module may determine if the equipment willsurvive the mission before committing the ship or military vehicle tothe mission, thereby avoiding a potentially catastrophic event. Theembedded condition-based maintenance software/models provides automatedhealth monitoring of the motor and gear train, thus reducing the needfor regular maintenance checks and servicing by technicians. Theintegration of motor control and diagnostics on a single platform opensthe door to a new fault-tolerant control algorithm that canintelligently trade-off system performance to avert failure.

These and other objects, advantages, and features of the invention willbecome apparent to those skilled in the art from the detaileddescription and the accompanying drawings. It should be understood,however, that the detailed description and accompanying drawings, whileindicating preferred embodiments of the present invention, are given byway of illustration and not of limitation. Many changes andmodifications may be made within the scope of the present inventionwithout departing from the spirit thereof, and the invention includesall such modifications. The scope of these changes will become apparentfrom the appended claims.

What is claimed is:
 1. A method of optimizing control and diagnosticsoperations performed on a controlled system, the method comprising:providing a diagnostics module and a control module, wherein each moduleis integrated with the controlled system; generating, with the controlmodule, a control signal in response to feedback from the controlledsystem; generating, with the diagnostics module and based on the controlsignal, a health assessment signal indicative of the health of thecontrolled system; predicting (1) when a controlled system failure willoccur, and (2) the cause of the controlled unit failure, wherein saidpredicting step is performed in response to said health assessment andcontrol signals; and determining whether to alter the control signalbased on said predicting step.
 2. The method according to claim 1,further comprising planning a change in the control signal in responseto said determining step, and altering the control signal based on saidplanning step.
 3. The method according to claim 2, further comprisingdetermining, prior to said altering step, the stability of thecontrolled system in response to said planning step.
 4. The methodaccording to claim 3, further comprising activating the controller priorto said altering step.
 5. The method according to claim 1, wherein thecontrol signal is based on a process constraint associated with thecontrolled system.
 6. The method according to claim 5, wherein theprocess constraint is an operational frequency to avoid.
 7. The methodaccording to claim 6, wherein the operational frequency to avoid isdetermined by the diagnostics module in response to feedback from thecontrolled system.
 8. The method according to claim 1, wherein thefeedback is generated by a sensor monitoring the controlled system.
 9. Amethod of optimizing control and diagnostics operations performed on amotor, the method comprising: providing a feedback control circuit and adiagnostics circuit, both of the circuits being integrated with themotor; generating a health assessment signal with the diagnosticscircuit, the health assessment signal being based on an output of thecontrol feedback circuit; generating a controller modification signalwith the diagnostics circuit wherein the controller modification signalis based on the health assessment signal; and generating a drivingoutput with the feedback control circuit based on feedback from themotor and the controller modification signal.
 10. The method accordingto claim 9, wherein the controller includes a velocity feedback loop anda torque feedback loop, wherein said velocity feedback loop generates acurrent command signal in response to the feedback from the motor, andsaid torque feedback loop generates the driving output in response tothe current command signal.
 11. The method according to claim 10,wherein the velocity feedback loop includes a sensor that is coupled tothe motor, the sensor generating the feedback signal.
 12. The methodaccording to claim 11, wherein the feedback is indicative of a velocityof the motor.
 13. The method according to claim 12, wherein saidgenerating a health assessment signal step is performed by using a modelassociated with the motor, the model embedded in a memory of diagnosticscircuit.
 14. A method of optimizing the useful life of a motor accordingto a preventive maintenance schedule that includes a plurality ofpreventive maintenance checkpoints, the method comprising: sensing amotor parameter during operation of the motor and generating acorresponding motor parameter signal; generating, with a controller, adriving output based on the motor parameter signal; diagnosing a healthcondition of the motor based on at least one of a group comprising (1)an operating objective, (2) the driving output, and (3) a processconstraint; predicting, based on the health condition, whether a motorfault condition will occur prior to the next preventive maintenancecheckpoint; and determining whether to alter the control signal inresponse to said predicting step.
 15. The method according to claim 14,wherein the controller includes a velocity feedback circuit and a torquefeedback circuit.
 16. The method according to claim 15, wherein saidtorque feedback circuit includes a P-I controller and an inverter. 17.The method according to claim 16, wherein said diagnosing step isperformed with a diagnostics module integrated with the motor.
 18. Themethod according to claim 17, wherein said controller is integrated withthe motor.
 19. The method according to claim 14, wherein said diagnosingstep is performed by using a model associated with the motor.
 20. Amethod of optimizing the useful life of a motor according to apreventive maintenance schedule that includes a plurality of preventivemaintenance checkpoints, the method comprising: sensing a motorparameter during operation of the motor and generating a correspondingmotor parameter signal; generating, with a controller, a driving outputbased on the motor parameter signal; diagnosing a health condition ofthe motor based on at least one of a group comprising (1) an operatingobjective, (2) the driving output, and (3) a process constraint;predicting, based on the health condition, whether a motor faultcondition will occur prior to the next preventive maintenancecheckpoint; and determining whether to alter the control signal inresponse to said predicting; wherein the operating objective isoperating the motor at 200% duty for a predetermined amount of time. 21.A method of optimizing the useful life of a motor according to apreventive maintenance schedule that includes a plurality of preventivemaintenance checkpoints, the method comprising: sensing a motorparameter during operation of the motor and generating a correspondingmotor parameter signal; generating, with a controller, a driving outputbased on the motor parameter signal; diagnosing a health condition ofthe motor based on at least one of a group comprising (1) an operatingobjective, (2) the driving output, and (3) a process constraint;predicting, based on the health condition, whether a motor faultcondition will occur prior to the next preventive maintenancecheckpoint; and determining whether to alter the control signal inresponse to said predicting; wherein the process constraint is a motoroperational frequency to avoid.
 22. The method according to claim 21,wherein the operational frequency to avoid is determined based on themotor parameter signal.
 23. A method of optimizing the useful life of amotor according to a preventive maintenance schedule that includes aplurality of preventive maintenance checkpoints, the method comprising:sensing a motor parameter during operation of the motor and generating acorresponding motor parameter signal; generating, with a controller, adriving output based on the motor parameter signal; diagnosing a healthcondition of the motor based on at least one of a group comprising (1)an operating objective, (2) the driving output, and (3) a processconstraint; predicting, based on the health condition, whether a motorfault condition will occur prior to the next preventive maintenancecheckpoint; and determining whether to alter the control signal inresponse to said predicting; wherein the process constraint is thecriticality of a mission including an allowable risk.
 24. A method ofoptimizing control and diagnostics operations performed on a controlledsystem, the method comprising: providing a diagnostics module and acontrol module, wherein each module is integrated with the controlledsystem; generating, with the control module, a driving output inresponse to feedback from the controlled system; generating, with thediagnostics module and based on the driving output, a diagnostics signalindicative of the health of the controlled system; determining anexpected time to fail based on the diagnostics signal; and predictingwhether the controlled system will operate according to a predeterminedset of criteria in response to said determining step.
 25. The method ofclaim 24, further comprising: planning a change in control based on saidpredicting step; determining a new expected time to fail in response tosaid planning step; and predicting whether the controlled unit willoperate according to the predetermined set of criteria in response tosaid determining a new expected time to fail step.
 26. The methodaccording to claim 25, further comprising: prescribing a change incontrol based on an operating objective; and predicting whether thecontrolled unit will operate according to the operating objective basedon said prescribing step.
 27. The method of claim 26, further comprisinggenerating a controller modification signal in response to at least oneof said predicting steps.
 28. The method of claim 27, further comprisingactivating the controller.
 29. The method of claim 25, wherein thepredetermined set of criteria includes controlled system operationaccording to a normal operating characteristic for a predeterminedamount of time.